Talk by Song-Chun Zhu of UCLA. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley on November 12, 2009.


Abstract.

Images and video are very high dimensional signals that reside in a wide spectrum of manifolds/subspaces of varying dimensions. In this talk, I will discuss two types of pure manifolds:
(i) implicit manifolds for high entropy patterns, like textures, modeled by Markov random fields, and
(ii) explicit manifolds for low entropy patterns, like textons and primitives, modeled by sparse coding.

I will show that these manifolds are connected through scaling (zooming), and present a unifying theory for learning probabilistic models by manifold pursuit through information projection. Then I will discuss how these manifolds are mixed to form middle entropy patterns, such as object templates, and integrated to generate a primal sketch representation for generic images as conjectured by David Marr in his influential book.

I will also show ongoing work on video primal sketch which integrates trackable motion, intrackable motion, and human actions.